首页|Laboratory for Advanced Materials Researchers Describe New Findings in Machine L earning (Classification of Progressive Wear on a Multi-Directional Pin-on-Disc T ribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acou stic ...)
Laboratory for Advanced Materials Researchers Describe New Findings in Machine L earning (Classification of Progressive Wear on a Multi-Directional Pin-on-Disc T ribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acou stic ...)
扫码查看
点击上方二维码区域,可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from Thun, Switzerland, by NewsRx journalists, research stated, "Human joint prostheses experience wear failure d ue to the complex interactions between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo)." Funders for this research include Empa Internal; Robert Mathys Foundation. Our news reporters obtained a quote from the research from Laboratory for Advanc ed Materials: "This study uses the wear classification to investigate the gradua l and progressive abrasive wear mechanisms in UHMWPE. Pin-on-disc tests were con ducted under simulated in vivo conditions, monitoring wear using Acoustic Emissi on (AE). Two Machine Learning (ML) frameworks were employed for wear classificat ion: manual feature extraction with ML classifiers and a contrastive learning-ba sed Convolutional Neural Network (CNN) with ML classifiers. The CNN-based featur e extraction approach achieved superior classification performance (94% to 96%) compared to manual feature extraction (81% to 89%). The ML techniques enable accurate wear classification, aidin g in understanding surface states and early failure detection."
Laboratory for Advanced MaterialsThunSwitzerlandEuropeCyborgsEmerging TechnologiesMachine Learning